




"""simple docstring"""




from collections import deque







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  Optional[Any]								=       len(__UpperCamelCase	)
			__lowercase			:  Optional[Any]								=       deque()
			__lowercase			:  Optional[int]								=       [False for _ in range(__UpperCamelCase	)]
			__lowercase			:  List[Any]								=       [-1 for _ in range(__UpperCamelCase	)]
			__lowercase			:  List[Any]								=       index_of[:]

			def strong_connect(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	):
						__lowercase			:  str								=       index  # the number when this node is seen
						__lowercase			:  Dict								=       index  # lowest rank node reachable from here
						index += 1
						stack.append(__UpperCamelCase	)
						__lowercase			:  Optional[int]								=       True

						for w in g[v]:
									if index_of[w] == -1:
												__lowercase			:  int								=       strong_connect(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)
												__lowercase			:  Tuple								=       (
												    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
												)
									elif on_stack[w]:
												__lowercase			:  List[str]								=       (
												    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
												)

						if lowlink_of[v] == index_of[v]:
									__lowercase			:  Optional[int]								=       []
									__lowercase			:  List[Any]								=       stack.pop()
									__lowercase			:  Any								=       False
									component.append(__UpperCamelCase	)
									while w != v:
												__lowercase			:  Optional[Any]								=       stack.pop()
												__lowercase			:  Optional[Any]								=       False
												component.append(__UpperCamelCase	)
									components.append(__UpperCamelCase	)
						return index

			__lowercase			:  Optional[int]								=       []
			for v in range(__UpperCamelCase	):
						if index_of[v] == -1:
									strong_connect(__UpperCamelCase	,				0	,				__UpperCamelCase	)

			return components







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			__lowercase			:  Union[str, Any]								=       [[] for _ in range(__UpperCamelCase	)]
			for u, v in edges:
						g[u].append(__UpperCamelCase	)
			return g


if __name__ == "__main__":
			# Test
			a_        =       7
			a_        =       [0, 0, 1, 2, 3, 3, 4, 4, 6]
			a_        =       [1, 3, 2, 0, 1, 4, 5, 6, 5]
			a_        =       [(u, v) for u, v in zip(source, target)]
			a_        =       create_graph(n_vertices, edges)

			assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)







'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						:  str				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Optional[int]				  =						{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: List[str]       =    """lilt"""






							def __init__(       self       ,   __snake_case=3_0522       ,   __snake_case=768       ,   __snake_case=12       ,   __snake_case=12       ,   __snake_case=3072       ,   __snake_case="gelu"       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=512       ,   __snake_case=2       ,   __snake_case=0.02       ,   __snake_case=1e-12       ,   __snake_case=0       ,   __snake_case="absolute"       ,   __snake_case=None       ,   __snake_case=4       ,   __snake_case=1024       ,   **__snake_case       ,   ):
											super().__init__(pad_token_id=__snake_case       ,   **__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= num_hidden_layers
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= num_attention_heads
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_act
											_SCREAMING_SNAKE_CASE					:			Dict							= intermediate_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= hidden_dropout_prob
											_SCREAMING_SNAKE_CASE					:			List[str]							= attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= max_position_embeddings
											_SCREAMING_SNAKE_CASE					:			Dict							= type_vocab_size
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= initializer_range
											_SCREAMING_SNAKE_CASE					:			Tuple							= layer_norm_eps
											_SCREAMING_SNAKE_CASE					:			str							= position_embedding_type
											_SCREAMING_SNAKE_CASE					:			List[str]							= classifier_dropout
											_SCREAMING_SNAKE_CASE					:			int							= channel_shrink_ratio
											_SCREAMING_SNAKE_CASE					:			str							= max_ad_position_embeddings




